code: def run_and_save_benchmark(
      layer: LayerType,
      batch_size: int,
      gsm_mode: str,
      args,
      layer_config: Dict[str, Any],
      root: str = "./results/raw/",
      suffix: str = "",
  ) -> None:
      """Runs and saves (if desired) the benchmark for the given layer and batch size.
  
      Args:
          layer: the layer to run benchmarks for
          batch_size: the batch size to run benchmarks for
          args: additional arguments
          layer_config: the settings for this layer
          root: directory to write results to
          suffix: optional string to append to result file name
      """
  
      logger.info(
          f"Benchmarking {layer} {gsm_mode} layer with batch size {batch_size}."
      )
      results = []
  
      for i in range(args.num_runs):
          runtime, memory_stats = run_layer_benchmark(
              num_repeats=args.num_repeats,
              forward_only=args.forward_only,
              layer_name=layer,
              batch_size=batch_size,
              random_seed=args.random_seed + i
              if args.random_seed is not None
              else None,
              gsm_mode=gsm_mode,
              **layer_config,
          )
          res = {"runtime": runtime, "memory_stats": memory_stats}
          results.append(res)
          logger.info(res)
  
      # save the benchmark results if desired
      if not args.no_save:
          save_results(
              layer=layer,
              batch_size=batch_size,
              num_runs=args.num_runs,
              num_repeats=args.num_repeats,
              gsm_mode=gsm_mode,
              results=results,
              config=layer_config,
              random_seed=args.random_seed,
              forward_only=args.forward_only,
              root=root,
              suffix=suffix,
          )
  
file_path: opacus/benchmarks/run_benchmarks.py
imports: import argparse
  import itertools
  import json
  import logging
  from os.path import exists
  from typing import Any, Dict
  from benchmarks.benchmark_layer import run_layer_benchmark
  from benchmarks.layers import LayerType
  from benchmarks.utils import get_layer_set, get_path, save_results
label_desc: Use image node_id 1 for calling a global function with example usage: run_and_save_benchmark(layer, batch_size, gsm_mode, args, layer_config, root, suffix) without return types